Task semantics can be expressed by a set of input-to-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning from task instructions. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize the current research on instruction learning, particularly, by answering the following questions: (i) what is task instruction, and what instruction types exist? (ii) how to model instructions? (iii) what factors influence and explain the instructions' performance? (iv) what challenges remain in instruction learning? To our knowledge, this is the first comprehensive survey about textual instructions.

翻译：任务语义可以通过一组输入到输出的示例或一段文本指令来表达。自然语言处理（NLP）的传统机器学习方法主要依赖于大规模任务特定示例集的可获取性。这带来了两个问题：首先，收集任务特定的标注示例不适用于任务过于复杂或标注成本过高，或者系统需要立即处理新任务的情况；其次，这不够用户友好，因为最终用户可能更愿意在使用系统前提供任务描述而非一组示例。因此，学术界越来越关注一种新的NLP监督范式：从任务指令中学习。尽管其取得了显著进展，但仍存在一些学术界共同面临的难题。本综述论文旨在总结当前关于指令学习的研究，特别是通过回答以下问题：（i）什么是任务指令，存在哪些指令类型？（ii）如何对指令进行建模？（iii）哪些因素影响并解释了指令的性能？（iv）指令学习仍面临哪些挑战？据我们所知，这是首篇关于文本指令的全面综述。